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Computer Science > Computation and Language

arXiv:2203.14093 (cs)
[Submitted on 26 Mar 2022 (v1), last revised 29 Mar 2022 (this version, v2)]

Title:MQDD: Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain

Authors:Jan Pašek, Jakub Sido, Miloslav Konopík, Ondřej Pražák
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Abstract:This work proposes a new pipeline for leveraging data collected on the Stack Overflow website for pre-training a multimodal model for searching duplicates on question answering websites. Our multimodal model is trained on question descriptions and source codes in multiple programming languages. We design two new learning objectives to improve duplicate detection capabilities. The result of this work is a mature, fine-tuned Multimodal Question Duplicity Detection (MQDD) model, ready to be integrated into a Stack Overflow search system, where it can help users find answers for already answered questions. Alongside the MQDD model, we release two datasets related to the software engineering domain. The first Stack Overflow Dataset (SOD) represents a massive corpus of paired questions and answers. The second Stack Overflow Duplicity Dataset (SODD) contains data for training duplicate detection models.
Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG); Programming Languages (cs.PL); Software Engineering (cs.SE)
Cite as: arXiv:2203.14093 [cs.CL]
  (or arXiv:2203.14093v2 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2203.14093

Submission history

From: Jan Pašek [view email]
[v1] Sat, 26 Mar 2022 15:01:26 UTC (125 KB)
[v2] Tue, 29 Mar 2022 11:36:36 UTC (125 KB)
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